chapter  12
Predictive mapping of coral reef fish species and communities
WithSimon J. Pittman, Anders Knudby
Pages 18

Introduction At the spatial scales relevant to the routine movements of many coral reef-associated shes, habitats exist as a spatially heterogeneous terrain varying in structural attributes, biotic communities, and seascape context (Grober-Dunsmore et  al. 2009, Boström et  al. 2011). Seascape studies, focusing on the ecological consequences of spatial patterning, have revealed that both the patch structure and the terrain morphology inuence the geographical patterns of sh distributions and diversity at a range of spatial scales (Pittman et al. 2007a,b, Boström et al. 2011). In the past few decades, however, it has become evident that the physical structural complexity of shallow tropical seascapes is declining (Pandol et al. 2005). For instance, in the Caribbean Sea, multiple interacting stressors are associated with a decline in the abundance of live coral, particularly the large and architecturally complex branching species, and a measurable attening of the topographic complexity over the past 60 years (Gardner et al. 2003, Pandol et al. 2005, Alvarez-Filip et al. 2009, 2011). In many locations, the biotic assemblage composition has also changed in what is often referred to as a “phase shift” from coral to algal dominance (Done 1992, Mumby 2009), with reduced-diversity coral communities comprised of stress-tolerant species (Green et al. 2008). These changes, combined with shing pressure, have led to a regionwide decline in reef sh density (Paddack et al. 2009) and size composition, with the largest shes becoming increasingly rare (Stallings 2009). A decline in habitat suitability for

Introduction ................................................................................................................................. 219 History of predictive mapping in coral reef ecosystems ....................................................... 221 Why a multiscale approach? .....................................................................................................222 Spatial representations of seascape structure .........................................................................222 Machine-learning algorithms .................................................................................................... 224 A multiscale and multialgorithm comparative approach .....................................................225 Case studies of predictive mapping using sh-seascape relationships.............................. 226

Mapping habitat suitability for a harvested grouper species .......................................... 226 Mapping large-bodied shes to identify essential sh habitat ........................................227 Mapping indicators of coral reef resilience ........................................................................228 Forecasting the impact of declining reef complexity on sh distributions ....................228